This is a repository copy of Diverse supraglacial drainage patterns on the Devon ice Cap, Arctic Canada.

White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/166803/

Version: Published Version

Article: Lu, Y., Yang, K., Lu, X. et al. (5 more authors) (2020) Diverse supraglacial drainage patterns on the Devon ice Cap, Arctic Canada. Journal of Maps, 16 (2). pp. 834-846. ISSN 1744-5647 https://doi.org/10.1080/17445647.2020.1838353

Reuse This article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: https://creativecommons.org/licenses/

Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request.

[email protected] https://eprints.whiterose.ac.uk/ Journal of Maps

ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tjom20

Diverse supraglacial drainage patterns on the Devon ice Cap, Arctic Canada

Yao Lu , Kang Yang , Xin Lu , Laurence C. Smith , Andrew J. Sole , Stephen J. Livingstone , Xavier Fettweis & Manchun Li

To cite this article: Yao Lu , Kang Yang , Xin Lu , Laurence C. Smith , Andrew J. Sole , Stephen J. Livingstone , Xavier Fettweis & Manchun Li (2020) Diverse supraglacial drainage patterns on the Devon ice Cap, Arctic Canada, Journal of Maps, 16:2, 834-846, DOI: 10.1080/17445647.2020.1838353 To link to this article: https://doi.org/10.1080/17445647.2020.1838353

© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of Journal of Maps

View supplementary material

Published online: 09 Nov 2020.

Submit your article to this journal

Article views: 135

View related articles

View Crossmark data

Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=tjom20 JOURNAL OF MAPS 2020, VOL. 16, NO. 2, 834–846 https://doi.org/10.1080/17445647.2020.1838353

Science Diverse supraglacial drainage patterns on the Devon ice Cap, Arctic Canada Yao Lu a, Kang Yang a,b,c, Xin Lu a, Laurence C. Smithd,e, Andrew J. Sole f, Stephen J. Livingstone f, Xavier Fettweisg and Manchun Lia,b aSchool of Geography and Ocean Science, Nanjing University, Nanjing, People’s Republic of China; bJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing, People’s Republic of China; c Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai, People’s Republic of China; dInstitute at Brown for Environment and Society, Brown University, Providence, RI, USA; eDepartment of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA; fDepartment of Geography, University of Sheffield, Sheffield, UK; gDepartment of Geography, University of Liège, Liège, Belgium

ABSTRACT ARTICLE HISTORY The Devon Ice Cap (DIC) is one of the largest ice masses in the Canadian Arctic. Each summer, Received 30 August 2019 extensive supraglacial river networks develop on the DIC surface and route large volumes of Revised 2 June 2020 meltwater from ice caps to the ocean. Mapping their extent and understanding their Accepted 14 October 2020 ff temporal evolution are important for validating runo routing and melt volumes predicted KEYWORDS by regional climate models (RCMs). We use 10 m Sentinel-2 images captured on 28 July and 2 Supraglacial rivers; regional 10/11 August 2016 to map supraglacial rivers across the entire DIC (12,100 km ). Both climate model (RCM); dendritic and parallel supraglacial drainage patterns are found, with a total length of Sentinel-2; Devon ice Cap; −1 44,941 km and a mean drainage density (Dd) of 3.71 km . As the melt season progresses, Arctic Dd increases and supraglacial rivers form at progressively higher elevations. There is a positive correlation between RCM-derived surface runoff and satellite-mapped Dd, suggesting that supraglacial drainage density is primarily controlled by surface runoff.

1. Introduction large volumes of surface meltwater are transferred to The Arctic contains numerous large ice masses includ- the ice bed, influencing the evolution of subglacial ing the Greenland Ice Sheet, Devon Ice Cap (DIC) and hydrological systems and ice flow dynamics (Chu, Agassiz Ice Cap, with a total collective area of approxi- 2014; Iken & Bindschadler, 1986; Pitcher & Smith, mately two million square kilometers (Abdalati et al., 2019; Röthlisberger & Iken, 1981; Schoof, 2010; Sundal 2004; Cook et al., 2019; Gardner et al., 2011). The Arc- et al., 2011). Although the spatial structure of these tic is also highly sensitive to global climate change supraglacial river networks is thought to be controlled (Screen & Simmonds, 2010), and is currently warming by the surface topography (Yang et al., 2015a) through at roughly twice the global average rate, with annual the transfer of bed variability to the ice surface at the air temperatures for October 2015 to September large scale (Crozier et al., 2018; Ignéczi et al., 2018), 2016 averaging +2.0°C warmer than the 1981–2010 their temporal evolution is affected by the rate and average (Richter-Menge et al., 2016). Associated magnitude of meltwater production and the per- mass loss from Arctic ice masses contributed ∼31% meability of glacier surface throughout the melt season of observed global sea level rise between 1992 and (Irvine-Fynn et al., 2011; Lampkin & Vanderberg, 2017 (Box et al., 2018), with annual ice mass losses 2014). − averaging 50.2 Gt a 1 in the Canadian Arctic, over In general, large supraglacial river networks have twice the pre-1996 average (Noël et al., 2018). It is only recently begun attracting scientific attention therefore essential to understand factors that affect with most studies focused on the Greenland Ice the prevalence, spatial pattern, and transport of sur- Sheet (Colgan et al., 2011; Crozier et al., 2018; Gleason face meltwater from Arctic ice masses to oceans. et al., 2016; Ignéczi et al., 2018; King et al., 2016; During 2016, the warmest year in the Arctic since Lampkin & Vanderberg, 2014; Legleiter et al., 2014; 1900 (Richter-Menge et al., 2016), Arctic ice masses Pitcher & Smith, 2019; Poinar et al., 2015; Smith experienced significant surface melt, which formed et al., 2015; Smith et al., 2017; Yang & Smith, 2016). large and complex supraglacial river networks at Yang et al. (2019a) mapped small areas of the Devon their lower elevations in summer (Pitcher & Smith, and Barnes Ice Caps (as well as the northwest Green- 2019). Where supraglacial rivers terminate in moulins, land Ice Sheet) using 10 m Sentinel-2 visible/near-

CONTACT Kang Yang [email protected] School of Geography and Ocean Science, Nanjing University, Nanjing, People’s Republic of China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing, People’s Republic of China; University Corporation for Polar Research, Beijing, People’s Republic of China Supplemental data for this article can be accessed https://doi.org/10.1080/17445647.2020.1838353 © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of Journal of Maps This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrest- ricted use, distribution, and reproduction in any medium, provided the original work is properly cited. JOURNAL OF MAPS 835 infrared satellite images and found that the drainage patterns and densities of supraglacial river networks may differ significantly within and between different ice masses. Previous studies have documented supraglacial river formation on the DIC (Fernandes et al., 2018; Wyatt, 2013; Wyatt & Sharp, 2015; Yang et al., 2019a). Wyatt (2013) manually digitized supraglacial rivers across the DIC from Landsat 7 ETM+ imagery acquired during the 1999 summer, revealing that both surface topography and ice dynamics affect supraglacial drai- nage patterns. Yang et al. (2019a) mapped a snapshot of supraglacial rivers on the western and southern DIC, demonstrating the strong potential of 10 m Senti- nel-2 imagery to investigate supraglacial drainage pat- terns. However, the spatial distribution, drainage patterns and seasonal evolution of supraglacial rivers across the entire DIC and how their formation is Figure 1. Location of the Devon Ice Cap in the Canadian affected by surface runoff remain poorly understood. Arctic. Here, we build upon the preliminary study of Yang et al. (2019a) using Sentinel-2 satellite images to map from 2005–2009 than from 1963–2005 (Sharp et al., supraglacial river networks and drainage across the 2011). entire DIC, their mid- to late-season evolution, and The main ice cap has a dome-like shape with a ff response to surface runo . First, we extract the supra- maximum elevation of 1921m (Dowdeswell et al., glacial rivers from satellite images by integrating 2004). Eight major drainage basins (471–2,623 km2), cross-sectional and longitudinal open-channel mor- obtained from the Randolph Glacier Inventory 6.0 phometry. Then, we calculate the supraglacial drai- (RGI Consortium, 2017), cover 78% of the total area ff nage density within di erent elevation bands and of DIC (Figure 2)(Table 2). There are strong spatial drainage basins to investigate the evolution of supra- gradients in mass balance, glacial geometry and ice glacial rivers across western, eastern and southern flow across the ice cap (Burgess & Sharp, 2004; Mair DIC. Finally, we compare the satellite-mapped supra- et al., 2005), causing different DIC drainage basins ff glacial drainage density (Dd) with ice surface runo to exhibit distinct melt characteristics and hydrologic modeled from Modèle Atmosphérique Régional conditions (Burgess & Sharp, 2004). (MAR), a regional climate model (RCM), which Because of the lack of cloud-free image coverage, ff ff reveals how surface runo a ects the growth of the we only investigated the mid- to late-season evolution supraglacial rivers. We conclude by discussing the of supraglacial rivers on the western, southern and implications of the observed supraglacial drainage pat- eastern DIC. Only the low-elevation area (below terns and the relationship between satellite-mapped 800 m) on the eastern DIC (covering 5% of eastern ff Dd and RCM-derived surface runo estimates. DIC and 2% of these three areas) contains crevasses observed by the Sentinel-2 imagery used herein. 2. Study area The DIC is located between 74.5°N and 75.8°N and 3. Data and methods 80.0°W and 86.0°W in the Canadian Arctic (Figure 1). 3.1. Sentinel-2 Multi-Spectral Instrument Its total area is 12,100 km2, with an estimated ice volume images of 3,980 km3 and maximum ice thickness of 880 m (Dowdeswell et al., 2004). Climate warming has greatly Landsat images have been used for extracting supra- impacted the DIC in recent decades. Increases in sum- glacial rivers on both the Greenland and Antarctic mertime temperature and lengthening melt seasons ice sheets (Bell et al., 2017; Kingslake et al., 2017; have intensified surface ablation and overall mass loss Lampkin & Vanderberg, 2014; Poinar et al., 2015; (Boon et al., 2010; Burgess et al., 2005; Burgess & Yang & Smith, 2016). However, because supraglacial Sharp, 2004; Burgess & Sharp, 2008; De Jong et al., rivers are normally less than 30 m wide (the spatial 2018; Gascon et al., 2013a; Shepherd et al., 2007). resolution of multi-spectral Landsat images) these Both the total volume and area of the DIC are declining data can delineate large main stems but miss narrow ∼− 3 −1 ∼− 2 −1 ( 1.7 km a ; 8.3 km a, respectively) (Boon tributaries. To extract more representative supragla- et al., 2010; Burgess & Sharp, 2004; Mortimer et al., cial river networks, higher spatial resolution images 2016), and its annual mass loss was six times higher are required (Colgan et al., 2011; Karlstrom & Yang, 836 Y. LU ET AL.

Figure 2. Sentinel-2 images (acquired on 28 July 2016, RGB: bands 4 (Red), 3 (Green), 2 (Blue)) of the entire Devon Ice Cap (exclud- ing the southwest arm). (a–f) show typical areas in different DIC basins (red rectangles in center panel (g)). Black lines mark the boundaries of the major drainage basins (numbered as in Table 2).

2016; Smith et al., 2015; Smith et al., 2017; Yang & atmosphere reflectance) with sub-pixel multispectral Smith, 2013). Ultra high resolution images (meter- registration (Baillarin et al., 2012), provided freely by scale), such as GeoEye and WorldView-1/2/3/4, can ESA’s Scientific Data Hub (https://scihub.copernicus. allow accurate delineation of supraglacial rivers, but eu/dhus/#/home). their high cost and limited spatial coverage reduce Nine Sentinel-2 images were acquired to extract their usability in large-scale applications (Yang & supraglacial rivers across the DIC from late July Smith, 2013). through early August, the mid- to late- melt season As demonstrated by Yang et al. (2019a), the Senti- for Arctic ice masses in this area. These images nel-2A/B satellites (hereafter ‘Sentinel-2’) launched by include six from 28 July 2016 (Figure 2), two the European Space Agency (ESA) in 2015 and 2017 from 10 August 2016, and one from 11 August provide an exciting new opportunity to map supragla- 2016 (Table 1). Elevation contours were generated cial rivers over large geographic areas with a spatial from the 30 Arc-Second Elevation (GTOPO30) resolution intermediate between Landsat and World- digital elevation model, provided by the USGS View. Sentinel-2 has four spectral bands (blue, EarthExplorer (https://earthexplorer.usgs.gov/). green, red and near-infrared) at 10 m spatial resol- The 2 m ArcticDEM, obtained from Polar Geospa- ution. This multi-spectral capability enables calcu- tial Center (https://www.pgc.umn.edu/data/ lation of the normalized difference water index arcticdem/), was used to simulate supraglacial drai- (NDWI), which can differentiate surface water bodies nage patterns and compare with Sentinel-2 mapped from surrounding glacial ice and snow. We use the supraglacial rivers by using the ArcGIS hydrologic Sentinel-2 Level-1C product, (orthorectified top-of- analysis tools. JOURNAL OF MAPS 837

Table 1. Images used in this study. the surrounding ice background. Using a global Image Type Acquired Date Tile Number Data Source threshold (T = 5) based on the pixel brightness histo- Sentinel-2A MSI 2016.7.28 T16XEH ESA gram of the grayscale image, supraglacial rivers are 2016.7.28 T16XEJ ff 2016.7.28 T17XMC e ectively discerned and used to create a binary 2016.7.28 T16XMD water mask (Yang et al., 2015b). Finally, ArcScan for 2016.7.28 T16XME 2016.7.28 T16XND ArcGIS was used to generate river vectors from this 2016.8.10 T16XEJ binary water mask (Figure 3f). To quantify the ability 2016.8.10 T17XMD of the automated mapping technique to capture supra- 2016.8.11 T17XMD GTOPO30 - W100N90 USGS glacial rivers, we also manually digitized the supragla- ArcticDEM - 27_30 PGC cial rivers shown in Figure 3a. The overall spatial 27_31 28_29 pattern was very similar, but the sum length of the 28_30 automated extraction results is 13.6% shorter than 28_31 the manual digitization results, due to several gaps in automatically-mapped tributary rivers. 3.2. Supraglacial river extraction At high elevations, surface meltwater initially per- colates into snowpack pore spaces and then forms The complex physical and spectral characteristics of slush flows when saturation is reached (Irvine-Fynn bare ice make automated extraction of supraglacial et al., 2011; Pitcher & Smith, 2019). These slush rivers from multi-spectral satellite images challenging flows can transform into open meltwater channels (Yang & Smith, 2013). Here, we used the method of once the bare ice surface is exposed (Chu, 2014; Yang et al. (2015b) to extract rivers on the basis of Cuffey & Paterson, 2010; Pitcher & Smith, 2019). It their Gaussian-like brightness cross-section and longi- is challenging to distinguish slush flows from well- tudinal open-channel morphometry as follows: First, channelized supraglacial rivers in satellite imagery water features were enhanced by applying NDWI since they are both represented as bright, linear fea- (Mcfeeters, 1996), which differentiates meltwater tures in NDWI images. Therefore, in this study, we from the background ice or snow (Figure 3b). Second, consider these slush flows as initial supraglacial rivers low-frequency image background and high-frequency and do not distinguish between slush flows and open image noise were removed using a band-pass filter meltwater channels. − − ramped between 1/200 m 1 and 1/40 m 1 (Figure 3c). While we acknowledge that our optical satellite- Then, a Gabor filter was applied and the thickness of based study may be missing some key physical pro- the Gabor filter was set to 2 to enhance small rivers cesses (e.g. firn stratigraphy) not visible from space, (river widths smaller than two pixels) (Yang et al., our study does offer an efficient method for mapping 2015b). The Gabor filter can enhance linear features temporal and spatial variations in supraglacial river in different directions and at different scales, which networks from space. effectively amplifies the river cross-sections (Figure 3d). To improve river connectivity (Figure 3e) 3.3. RCM-derived surface runoff by accounting for brightness variations along the river courses and issues with disconnected river segments, The Modèle Atmosphérique Régional (MAR) is a path opening operator was then applied (Heijmans among the best RCM over the Greenland Ice Sheet et al., 2005). Path opening is a flexible mathematical (Fettweis et al., 2017; Fettweis et al., 2020). MAR was morphology operator, which homogenizes the bright- used to model surface runoff across the DIC by simu- ness of pixels along linear features in the image by lating temperature, precipitation, and refreezing vari- designing a slender, directional path operator with a ables in 7.5 × 7.5 km grid cells. MAR version 3.8 was fi xed distance (Lmin = 20 pixels) (Figure 3e). used in this study and forced at the lateral boundaries After Gabor filtering and path opening, the supra- by 6-hourly ERA-Interim reanalysis outputs (Delhasse glacial rivers become far easier to differentiate from et al., 2018). MAR integrates a runoff delay function to represent the time lag between surface meltwater pro- Table 2. Supraglacial hydrological features of the major duction and transportation to nearby supraglacial riv- drainage basins across the DIC on 28 July 2016. ers (Lefebre et al., 2003; Zuo & Oerlemans, 1996). Basin Area Drainage density RCM-derived surface However, the MAR delay function temporally- − ID (km2) (km 1) runoff (mm) smooths the MAR runoff signal resulting in less realis- 1 1046.46 5.71 9.33 tic results than the runoff calculated directly from the 2 765.44 1.63 5.66 ff 3 663.92 2.51 8.26 raw model data (Runo = Melt + Rain - Refreezing) 4 1134.44 2.06 12.03 (Yang et al., 2019b). The uncertainty is about 30% of 5 572.17 3.09 13.97 ff 6 471.08 2.46 10.62 runo value (Fettweis et al., 2017). In this study, we 7 2622.81 1.94 10.42 therefore calculated ice surface (runoff) directly from 8 2170.55 2.19 6.62 MAR on 28 July 2016 and 10/11 August 2016, to 838 Y. LU ET AL.

Figure 3. Supraglacial river extraction in Sentinel-2 image acquired on 28 July 2016, following the method of (Yang et al., 2015b). (a) Original Sentinel-2 true color image, (b) NDWI image, (c) NDWI image after band-pass filtering, (d) image after Gabor filtering, (e) image after path opening and (f) final supraglacial river extraction results and manual digitization of the same image. yield the total amount of daily runoff generated within the DIC was covered by supraglacial rivers and only − each grid cell (mm day 1) based on surface mass bal- 0.1% of the DIC was covered by supraglacial lakes at ance variables alone with no routing delay imposed. this time. The total mapped river length was 4.5×104 ff −1 MAR grid cells were intersected with di erent vector km and the average Dd was 3.71 km . The highest polygon boundaries (elevation bands, drainage basins) elevation at which supraglacial rivers were mapped and the total runoff computed by summing the inter- was ∼1500 m. sected runoff values. The flat western basin of the DIC (Basin 1) was −1 Drainage density (Dd) was calculated by dividing found to have the highest Dd (5.71 km ), with well- the total river length by the area of the corresponding developed supraglacial river networks (Figure 4a). At basin/elevation band (Yang et al., 2016). In section 4.3, elevations between 1100 and 1300 m, meltwater is we compare satellite-mapped Dd with RCM-derived not clearly channelized and represents braided and surface runoff above 600 m, because the areas of broadly dendritic supraglacial rivers. As the meltwater elevation bands near the ice cap margin (below flows to lower elevations, the supraglacial rivers 600 m) are small and supraglacial Dd below 600 m is become entrenched and comprise of many small rivers influenced by multiple factors (e.g. crevasses and parallel to each other (Figure 4a). At elevations below dust zone) rather than only surface runoff (Lea & 1000 m, supraglacial drainage becomes organized into Brough, 2019). fewer large rivers and Dd decreases (Figure 4g, Figure S1). On the northwest DIC (Basin 2), with the lowest Dd − 4. Results (1.63 km 1), most supraglacial rivers converge into several large supraglacial rivers at the elevation 4.1. Mapping of supraglacial rivers across the between 1100 and 1400 m, then continue to flow Devon Ice Cap down the ice surface to lower elevation (rather than Supraglacial rivers were successfully mapped across terminate in supraglacial lakes or moulins) the entire DIC using nine Sentinel-2 L1C images (Figure 4b). Meanwhile, some small sub-parallel riv- acquired on 28 July 2016 (Main Map, Figure 4). ers, which initiate at ∼1000 m, are distributed Diverse supraglacial drainage patterns and densities from ∼1000 m to ∼600 m near the ice cap margin were found in different DIC basins. In total, 5.5% of (Figure 4b, g). JOURNAL OF MAPS 839

Figure 4. Supraglacial river networks across the entire Devon Ice Cap mapped from 2 m ArcticDEM and six 10 m Sentinel-2 images (acquired on 28 July 2016), for everywhere except the bottom right (southeastern DIC) due to the lack of Sentinel-2 images cover- age. (a–f) show examples of supraglacial river networks in different DIC basins (red rectangles in center panel (g)).

2 On the northeastern DIC (Basin 3–6), Dd values 4c) and one supraglacial lake (area 0.06 km )was − range from 2.06–3.06 km 1. Supraglacial rivers initiate located at ∼1200 m in Basin 8 (Figure 4e). Supraglacial at ∼1400 m and form dendritic drainage patterns on rivers at the highest elevation were located in Basin 8 the main cap, transforming into subparallel arrange- at 1500 m, matching previous observations from the ments on outlet glaciers at lower elevations, with 2015 melt season (Fernandes et al., 2018). meltwater routed parallel to the outlet glacier trunk Comparing satellite-mapped supraglacial rivers (Figure 4d). with ArcticDEM-modeled supraglacial rivers, we Basins 7 and 8, on the southern DIC, are the two find that the satellite-mapped supraglacial drainage largest drainage basins (2,171 km2 and 2,623 km2, patterns are similar to ArcticDEM-modeled drainage respectively), and exhibit relatively low Dd (1.94 and patterns, which implies that the supraglacial drainage − 2.19 km 1, respectively). Most supraglacial rivers are patterns are strongly controlled by surface topography dendritic (Figures 4c, e), apart from the western por- (Figure 4). However, ArcticDEM-modeled drainage tion of Basin 8, where supraglacial rivers are sub-par- networks do not reflect the true distribution of supra- allel and have low sinuosity (Figure 4f). Two small glacial rivers. Instead, they tend to overestimate supraglacial lakes (area 0.03 and 0.08 km2) were high-elevation rivers (which may be channelized but located at ∼1400 m and ∼1250 m in Basin 7 (Figure dry at the time of image capture) and underestimate 840 Y. LU ET AL. low-elevation rivers (due to the choice of the accumu- from 1200 m to 1400 m (Figure 5d). By the 10 August, lation area threshold) (Figure 4). snow-covered areas had become slush fields at 1200– 1500 m (Pitcher & Smith, 2019), with many poor- channelized supraglacial rivers with low sinuosity 4.2. Mid- to late-season evolution of forming (Figures 5a, Figure 6), with a maximum Dd supraglacial rivers − of 11.62 km 1 at this elevation band. At 1100– Mid- to late-season evolution of supraglacial rivers on 1200 m, the slush field transitioned into relatively the western, eastern, and southern DIC (parts of impermeable bare ice surface from 28 July to 10 Basins 1, 7 and 8) was analyzed by comparing the August (Irvine-Fynn et al., 2011), the supraglacial riv- supraglacial drainage densities on 28 July and 10/11 ers were better channelized and the Dd decreased from – −1 August 2016. Between these dates, Dd of these three 11.32 5.66 km . Below 1100 m, the Dd decreased by − areas increased: western DIC increasing from more than 50% (from 8.23–2.27 km 1). Although the − − 4.92 km 1–5.99 km 1; southern DIC from main rivers remained relatively stable across this − − 0.98 km 1–3.18 km 1; and eastern DIC from period, many small supraglacial rivers had either − − 0.80 km 1–9.59 km 1 (Figure 5). As melting contin- dried up or became narrow, and thus could not be dis- ued, Dd varied markedly with elevation and supragla- tinguished by Sentinel-2 images (Figures 5a, d). cial rivers gradually extended toward higher On the southern and eastern DIC, the evolution of elevations. The snow line increased from ∼1200 m supraglacial rivers differed from that on the western to ∼1500 m during 28 July and 10/11 August 2016 DIC. On the southern DIC (a part of Basin 8), Dd (Figure 6), and the daily runoff was clearly higher on was generally lower, and peak Dd was located at 10/11 August than that on 28 July at 1300–1400 m 1300 m. As the snow surface diminished at 1100- (Figure 7), which coincided with the largest increase 1400 m between 28 July and 10 August, a large num- in supraglacial Dd occurred. ber of initial supraglacial rivers formed and the Dd – −1 From 28 July to 10 August 2016, the total Dd of increased by a factor of three (from 0.98 3.18 km ) supraglacial rivers on the western DIC (a part of (Figure 5b, e, Figure 6). This trend was also clearly − Basin 1) increased slightly from 4.92–5.99 km 1 and observed on the eastern DIC (a part of Basin 7), the snow covered fraction decreased from 0.39–0.14 where Dd on 11 August was 12 times higher than − (Figures 5a, d, Figure 6). Supraglacial rivers expanded that on 28 July (from 1.14–21.29 km 1)(Figure 5c, f) to higher elevations with the peak value of Dd moving and the snow cover fraction decreased from

Figure 5. Supraglacial rivers evolution. (a–c) show the supraglacial river networks extraction results, and (d–f) are corresponding supraglacial drainage density in different elevation bands. JOURNAL OF MAPS 841

Figure 6. Snow cover on the western, southern and eastern DIC delineated by thresholding the Sentinel-2 NIR bands (Ryan et al., 2019). The red lines show the boundaries of the snow and bare ice on 28 July and 10/11 August 2016.

0.40–0.05 (Figure 6). The location of the peak Dd where Dd is highest, no direct relationship is apparent fi extended from 1200 m to 1300 m (Figures 5c, f). Over- between R and Dd (Figure 9a), due to the signi cant ∼ all, on the southern and eastern DIC, a large number decrease in Dd from 1000 m to the ice cap margin. of newly formed supraglacial rivers were observed at In Basin 3, as R increases, Dd at elevations above both high and low elevations in contrast to the 900 m rise faster than at elevations below 900 m −1 −1 −1 −1 decrease in Dd at low elevations (<1200 m) observed (0.52 km mm vs. 0.15 km mm )(Figure 9c). on the western DIC. Compared to Basin 2 and Basins 4–8, each 1 mm increase in R of Basin 3 causes a greater increase in −1 −1 Dd (0.37 km vs. 0.13–0.24 km )(Figure 9c). In Basin 3, the area below 900 m comprises a topographi- 4.3. Comparing satellite-mapped Dd with RCM-derived surface runoff cally-constrained outlet glacier approximately 25 km in length, where surface water may be concentrated. There is a significantly positive linear relationship Nearly all supraglacial rivers drain into this outlet gla- between the RCM-derived surface runoff (R) and the cier rather than terminating on land or in supraglacial satellite-mapped Dd, calculated at a range of elevations lakes and moulins. This is in contrast to outlet glaciers on 28 July 2016 (Figure 8). Across the entire DIC, the in other basins, which only exist below 600 m (Boon relationship between R and Dd can be expressed by the et al., 2010) and are therefore not included in the stat- 2 linear function Dd = 0.20 R + 0.05 (r = 0.90, p < 0.01). istics (Figure 2g). −1 In other words, Dd increases by 0.2 km with each 1 mm increase in R. Both R and Dd gradually increase when moving from the high elevation interior (> 5. Discussion 1700m) to lower elevation areas (∼700 m) near the margin of the DIC (Figure 8). The overall DIC supraglacial drainage pattern is Different drainage basins demonstrate variations in characterized by high density, dendritic and parallel the relationship between R and Dd (Figure 9). In drainage networks. On both the eastern and southern Basins 2–8, significantly positive linear relationships DIC, dendritic drainage patterns dominate, which is in 2 (R > 0.68, p < 0.01) exist between R and Dd (Figures agreement with patterns observed by previous studies 9b–h). With each 1 mm increase in R, the Dd increases (Dowdeswell et al., 2004; Fernandes et al., 2018; Wyatt, − by 0.13–0.37 km 1 in Basin 2–8. However, in Basin 1, 2013; Wyatt & Sharp, 2015). However, only a few 842 Y. LU ET AL.

Figure 7. Daily RCM-derived surface runoff for 1300–1400 m elevation band of western, southern and eastern DIC during the 2016 melt season. studies have focused on the western DIC (Basin 1) Greenland close to the ice margin become fragmented fi (Wyatt, 2013; Yang et al., 2019a), where we nd paral- due to crevasses and moulins, and Dd would reduce, lel and dendritic drainage patterns and the highest Dd. resulting in a corresponding reduction in Dd. How- fi Meanwhile, there are signi cant decreases in Dd at low ever, due to the few crevasses that we observed on elevations on western DIC (Basin 1). Ignéczi et al. the western DIC, we attribute the low drainage density (2018) suggested that river networks on western mainly to the difficulty in detecting supraglacial rivers in the low albedo zone (Yang et al., 2019a). During the melt season, supraglacial rivers extend toward higher elevations on the DIC as the surface runoff increases at high elevation, similar to the evolution of supragla- cial rivers on the southwest Greenland Ice Sheet (Lampkin & Vanderberg, 2014). We have compared supraglacial rivers delineated in this study with supraglacial rivers delineated in Wyatt (2013). In general, our 10 m Sentinel-2 river map matches well. Notably, however, our Sentinel-2 map delineates numerous small supraglacial rivers that are not captured by Wyatt’s map and yields higher −1 −1 Dd (maximum: 12.5 km vs. 8.7 km ). This agrees with Yang et al. (2019a) who show that Sentinel-2 Figure 8. Relationship between daily RCM-derived surface images are superior to Landsat images for delineating runoff (R) and satellite-mapped drainage density (Dd)on28 July 2016. All R and Dd were calculated at each 100 m of small supraglacial rivers. In addition, the highest elevation (from 700 m to 2000m). supraglacial Dd is found on the western DIC (Basin JOURNAL OF MAPS 843

Figure 9. Relationship between daily RCM-derived surface runoff (R) and satellite-mapped drainage density (Dd) in eight major basins on 28 July 2016. All R and Dd were calculated at each 100 m of elevation (from 700 m to 2000m).

1), but was reported on the eastern DIC by Wyatt rivers to surface runoff. Moreover, the positive linear (2013). The highest-elevation of satellite-mapped relationship between surface runoff and supraglacial supraglacial rivers is located at 1500 m in Basin 7, Dd indicate that supraglacial Dd is primarily controlled which is 80 m higher than that reported by Wyatt by surface runoff. We suggest that this finding is impor- (2013) during the 1999 summer. It is probably that tant for better quantifying surface runoff.Morespecifi- with climate change, the occurrence of the further cally, runoff is the most difficult variable to qualify in inland melting and the change of the underlying sur- RCMs and is usually calculated as residuals of other face (ice/snow/firn) leads to the supraglacial rivers’ climatic variables (Smith et al., 2017); our study pro- expansion (Bezeau et al., 2013; Fisher et al., 2012; Gas- vides a potential approach to validate RCM-derived ff con et al., 2013b; Rutishauser et al., 2016; Sharp et al., surface runo using satellite-observed Dd. 2011; Wolken et al., 2009). We present a positive linear relationship between ff 6. Conclusion ice surface runo and supraglacial Dd, which has not been quantified previously. The slope of the linear This paper presents a first complete time-slice map of function between satellite-mapped Dd and RCM- > 10 m wide supraglacial river networks across the derived surface runoff in Basin 3 is higher than in entire Devon Ice Cap using high spatial resolution other basins, indicating that RCM may underestimate (10 m) Sentinel-2 L1C satellite images. On 28 July the surface runoff on outlet glaciers. Low-albedo 2016, supraglacial drainage density on the DIC was − exposed rocks that radiate longwave radiation may 3.71 km 1, with both dendritic and parallel drainage increase local melt rates (Kingslake et al., 2017). patterns evident in different drainage basins of the Further improving parameters of RCM is important ice cap, which will improve our understanding of the for better illustrating the response of supraglacial intensity and efficiency at which meltwater is being 844 Y. LU ET AL. transported from the ice cap. The basin with the high- Xin Lu http://orcid.org/0000-0001-6381-3672 est drainage density was located on the western DIC, Andrew J. Sole http://orcid.org/0000-0001-5290-8967 where peak drainage density was found at elevations Stephen J. Livingstone http://orcid.org/0000-0002-7240- 5037 between 1200–1400 m. By August 10/11, a large num- ber of new supraglacial rivers had formed at higher elevations, and the region of peak drainage density Reference had moved toward higher elevations. At low elevations (< 1200 m), drainage density decreased on the western Abdalati, W., Krabill, W., Frederick, E., Manizade, S., DIC but increased in both the southern and eastern Martin, C., Sonntag, J., Swift, R., Thomas, R., Yungel, J., DIC. Moreover, we found a positive relationship & Koerner, R. (2004). Elevation changes of ice caps in the Canadian Arctic Archipelago. Journal of Geophysical between satellite-mapped supraglacial drainage den- Research: Earth Surface, 109(F4), F04007. https://doi. sity and RCM-derived surface runoff. This indicates org/10.1029/2003JF000045 that the supraglacial drainage density is primarily con- Baillarin, S., Meygret, A., Dechoz, C., Petrucci, B., trolled by ice surface runoff, which will improve our Lacherade, S., Trémas, T., Isola, C., Martimort, P., & understanding of supraglacial hydrological processes Spoto, F.. (2012, July 22-27). Sentinel-2 level 1 products and image processing performances. 2012 IEEE and mass balance across the Arctic cryosphere. In International Geoscience and Remote Sensing the future, integrating remotely sensed observations, Symposium, Munich, Germany. RCM outputs and high spatial resolution DEMs (e.g. Bell, R. E., Chu, W., Kingslake, J., Das, I., Tedesco, M., Tinto, 2 m ArcticDEM) more closely will enable a better K. J., Zappa, C. J., Frezzotti, M., Boghosian, A., & Lee, W. understanding of the supraglacial drainage patterns S. (2017). Antarctic ice shelf potentially stabilized by and their response to ice surface runoff. export of meltwater in surface river. Nature, 544(7650), 344–348. https://doi.org/10.1038/nature22048 Bezeau, P., Sharp, M., Burgess, D., & Gascon, G. (2013). Firn fi Software pro le changes in response to extreme 21st-century melt- ing at Devon Ice Cap, Nunavut, Canada. Journal of ESRI ArcGIS 10.6.1 and Adobe Illustrator CC 2018 Glaciology, 59(217), 981–991. https://doi.org/10.3189/ were used in the production of the main map. 2013JoG12J208 Boon, S., Burgess, D. O., Koerner, R. M., & Sharp, M. J. (2010). Forty-seven years of research on the Devon – Acknowledgements Island ice cap, Arctic Canada. Arctic, 63(1), 13 29. https://doi.org/10.14430/arctic643 Kang Yang acknowledges the support of the National Key Box, J. E., Colgan, W. T., Wouters, B., Burgess, D. O., R&D Program (2018YFC1406101), the National Natural O’neel, S., Thomson, L. I., & Mernild, S. H. (2018). Science Foundation of China (41871327), and the Funda- Global sea-level contribution from Arctic land ice: mental Research Funds for the Central Universities. Laur- 1971–2017. Environmental Research Letters, 13(12), ence Smith acknowledges the NASA Cryospheric Science 125012. https://doi.org/10.1088/1748-9326/aaf2ed Program (80NSSC19K0942). Stephen Livingstone and Burgess, D. O., & Sharp, M. J. (2004). Recent changes in Andrew Sole acknowledge the support of a White Rose Uni- areal extent of the Devon ice cap, Nunavut, Canada. versity Consortium Collaboration Fund grant. We acknowl- Arctic, Antarctic, and Alpine Research, 36(2), 261–271. edge Faye Wyatt for providing a supraglacial river map of https://doi.org/10.1657/1523-0430(2004)036[0261: the DIC delineated from Landsat 7 ETM+ imagery acquired RCIAEO]2.0.CO;2 during 1999 summer. We thank the Associate Editor and Burgess, D. O., & Sharp, M. J. (2008). Recent changes in four reviewers (Martin Sharp, Caroline Clason, David Bur- thickness of the Devon Island ice cap. Canada. Journal gess and Daniele Cannatella) for their detailed and con- of Geophysical Research: Solid Earth, 113(B7), B07204. structive comments. https://doi.org/10.1029/2007JB005238 Burgess, D. O., Sharp, M. J., Mair, D. W., Dowdeswell, J. A., & Benham, T. J. (2005). Flow dynamics and iceberg cal- Disclosure statement ving rates of Devon Ice Cap, Nunavut, Canada. Journal of Glaciology, 51(173), 219–230. https://doi.org/10.3189/ fl No potential con ict of interest was reported by the author(s). 172756505781829430 Chu, V. W. (2014). Greenland ice sheet hydrology: A review. Progress in Physical Geography, 38(1), 19–54. https://doi. Funding org/10.1177/0309133313507075 ff This work was supported by the National Key R&D Pro- Colgan, W., Ste en, K., Mclamb, W. S., Abdalati, W., gram [Grant Number 2018YFC1406101], the National Rajaram, H., Motyka, R., Phillips, T., & Anderson, R. Natural Science Foundation of China [Grant Number (2011). An increase in crevasse extent, West Greenland: 41871327], and the Fundamental Research Funds for the Hydrologic implications. Geophysical Research Letters, Central Universities [Grant Number 0209-14380070]. 38(18), L18502. https://doi.org/10.1029/2011GL048491 Cook, A. J., Copland, L., Noël, B. P. Y., Stokes, C. R., Bentley, M. J., Sharp, M. J., Bingham, R. G., & Van Den Broeke, M. ORCID R. (2019). Atmospheric forcing of rapid marine-terminat- ing glacier retreat in the Canadian Arctic Archipelago. Yao Lu http://orcid.org/0000-0001-7843-1327 Science Advances, 5(3), eaau8507. https://doi.org/10. Kang Yang http://orcid.org/0000-0002-7246-8425 1126/sciadv.aau8507 JOURNAL OF MAPS 845

Crozier, J., Karlstrom, L., & Yang, K. (2018). Basal control of Gleason, C. J., Smith, L. C., Chu, V. W., Legleiter, C. J., supraglacial meltwater catchments on the Greenland Ice Pitcher, L. H., Overstreet, B. T., Rennermalm, A. K., Sheet. The Cryosphere, 12(10), 3383–3407. https://doi. Forster, R. R., & Yang, K. (2016). Characterizing supra- org/10.5194/tc-12-3383-2018 glacial meltwater channel hydraulics on the Greenland Cuffey, K. M., & Paterson, W. S. B. (2010). The physics of gla- Ice Sheet from in situ observations. Earth Surface ciers. Academic Press. Processes and Landforms, 41(14), 2111–2122. https://doi. De Jong, T., Copland, L., & Burgess, D. (2018). Changes in org/10.1002/esp.3977 glacier facies zonation on Devon Ice Cap, Nunavut, Heijmans, H., Buckley, M., & Talbot, H. (2005). Path detected from SAR imagery and field observations. The Openings and Closings. Journal of Mathematical Cryosphere Discuss, 2018,1–28. https://doi.org/10.5194/ Imaging and Vision, 22(2), 107–119. https://doi.org/10. tc-2018-250 1007/s10851-005-4885-3 Delhasse, A., Fettweis, X., Kittel, C., Amory, C., & Agosta, C. Ignéczi, Á, Sole, A., Livingstone, S., Ng, F., & Yang, K. (2018). Brief communication: Impact of the recent (2018). Greenland Ice Sheet surface topography and drai- atmospheric circulation change in summer on the future nage structure controlled by the transfer of basal variabil- surface mass balance of the Greenland Ice Sheet. The ity. Frontiers in Earth Science, 6, https://doi.org/10.3389/ Cryosphere, 12(11), 3409–3418. https://doi.org/10.5194/ feart.2018.00101 tc-12-3409-2018 Iken, A., & Bindschadler, R. A. (1986). Combined measure- Dowdeswell, J., Benham, T., Gorman, M., Burgess, D., & ments of subglacial water pressure and surface velocity of Sharp, M. (2004). Form and flow of the Devon Island Findelengletscher, Switzerland: Conclusions about drai- ice cap, Canadian Arctic. Journal of Geophysical nage system and sliding mechanism. Journal of Research: Earth Surface, 109(F2), F02002. https://doi. Glaciology, 32(110), 101–119. https://doi.org/10.1017/ org/10.1029/2003JF000095 S0022143000006936 Fernandes, L., Schmitt, A., Wendleder, A., Sharp, M., & Irvine-Fynn, T. D. L., Hodson, A. J., Moorman, B. J., Vatne, Roth, A.. (2018, June 4-7). Detecting Supraglacial G., & Hubbard, A. L. (2011). Polythermal glacier hydrol- Meltwater Drainage on the Devon Ice Cap using ogy: A review. Reviews of Geophysics, 49(4), RG4002. Kennaugh Decomposition of TerraSAR-X imagery. https://doi.org/10.1029/2010RG000350 EUSAR 2018 - 12th European Conference on Synthetic Karlstrom, L., & Yang, K. (2016). Fluvial supraglacial land- Aperture Radar, Aachen, Germany. scape evolution on the Greenland Ice Sheet. Geophysical Fettweis, X., Box, J. E., Agosta, C., Amory, C., Kittel, C., Research Letters, 43(6), 2683–2692. https://doi.org/10. Lang, C., Van As, D., Machguth, H., & Gallée, H. 1002/2016GL067697 (2017). Reconstructions of the 1900–2015 Greenland ice King, L., Hassan, M. A., Yang, K., & Flowers, G. (2016). sheet surface mass balance using the regional climate Flow routing for delineating supraglacial meltwater chan- MAR model. The Cryosphere, 11(2), 1015–1033. https:// nel networks. Remote Sensing, 8(12), 988. https://doi.org/ doi.org/10.5194/tc-11-1015-2017 10.3390/rs8120988 Fettweis, X., Hofer, S., Krebs-Kanzow, U., Amory, C., Aoki, Kingslake, J., Ely, J. C., Das, I., & Bell, R. E. (2017). T., Berends, C. J., Born, A., Box, J. E., Delhasse, A., Fujita, Widespread movement of meltwater onto and across K., Gierz, P., Goelzer, H., Hanna, E., Hashimoto, A., Antarctic ice shelves. Nature, 544(7650), 349–352. Huybrechts, P., Kapsch, M. L., King, M. D., Kittel, C., https://doi.org/10.1038/nature22049 Lang, C., … Zolles, T. (2020). GrSMBMIP: Lampkin, D., & Vanderberg, J. (2014). Supraglacial melt Intercomparison of the modelled 1980-2012 surface channel networks in the Jakobshavn Isbræ region during mass balance over the Greenland Ice sheet. The the 2007 melt season. Hydrological Processes, 28(25), Cryosphere Discuss., 2020,1–35. https://doi.org/10.5194/ 6038–6053. https://doi.org/10.1002/hyp.10085 tc-2019-321 Lea, J., & Brough, S.. (2019, December 9-13). Supraglacial Fisher, D., Zheng, J., Burgess, D., Zdanowicz, C., Kinnard, lake mapping of the entire Greenland Ice Sheet using C., Sharp, M., Bourgeois, J., & Bourgeois, M. (2012). Google Earth Engine. AGU Fall Meeting 2019, San Recent melt rates of Canadian Arctic ice caps are the Francisco, USA. highest in four millennia. Global and Planetary Change, Lefebre, F., Gallée, H., Van Ypersele, J.-P., & Greuell, W. 84-85,3–7. https://doi.org/10.1016/j.gloplacha.2011.06. (2003). Modeling of snow and ice melt at ETH Camp 005 (West Greenland): A study of surface albedo. Journal of Gardner, A. S., Moholdt, G., Wouters, B., Wolken, G. J., Geophysical Research: Atmospheres, 108(D8), 4231. Burgess, D. O., Sharp, M. J., Cogley, J. G., Braun, C., & https://doi.org/10.1029/2001JD001160 Labine, C. (2011). Sharply increased mass loss from gla- Legleiter, C. J., Tedesco, M., Smith, L. C., Behar, A. E., & ciers and ice caps in the Canadian Arctic Archipelago. Overstreet, B. T. (2014). Mapping the bathymetry of Nature, 473(7347), 357–360. https://doi.org/10.1038/ supraglacial lakes and streams on the Greenland ice nature10089 sheet using field measurements and high-resolution satel- Gascon, G., Sharp, M., Burgess, D., Bezeau, P., & Bush, lite images. The Cryosphere, 8(1), 215–228. https://doi. A. B. G. (2013b). Changes in accumulation-area firn stra- org/10.5194/tc-8-215-2014 tigraphy and meltwater flow during a period of climate Mair, D., Burgess, D., & Sharp, M. (2005). Thirty-seven year warming: Devon Ice Cap, Nunavut, Canada. Journal of mass balance of Devon Ice Cap, Nunavut, Canada, deter- Geophysical Research: Earth Surface, 118(4), 2380–2391. mined by shallow ice coring and melt modeling. Journal https://doi.org/10.1002/2013JF002838 of Geophysical Research: Earth Surface, 110(F1), F01011. Gascon, G., Sharp, M., & Bush, A. (2013a). Changes in melt https://doi.org/10.1029/2003JF000099 season characteristics on Devon Ice Cap, Canada, and Mcfeeters, S. K. (1996). The use of the normalized difference their association with the Arctic atmospheric circulation. water index (NDWI) in the delineation of open water fea- Annals of Glaciology, 54(63), 101–110. https://doi.org/10. tures. International Journal of Remote Sensing, 17(7), 3189/2013AoG63A601 1425–1432. https://doi.org/10.1080/01431169608948714 846 Y. LU ET AL.

Mortimer, C. A., Sharp, M., & Wouters, B. (2016). Glacier measurements of meltwater runoff on the Greenland ice surface temperatures in the Canadian high Arctic, sheet surface. Proceedings of the National Academy of 2000–15. Journal of Glaciology, 62(235), 963–975. Sciences, 114(50), E10622–E10631. https://doi.org/10. https://doi.org/10.1017/jog.2016.80 1073/pnas.1707743114 Noël, B., Van De Berg, W. J., Lhermitte, S., Wouters, B., Sundal, A. V., Shepherd, A., Nienow, P., Hanna, E., Palmer, Schaffer, N., & Van Den Broeke, M. R. (2018). Six decades S., & Huybrechts, P. (2011). Melt-induced speed-up of of glacial mass loss in the Canadian Arctic Archipelago. Greenland ice sheet offset by efficient subglacial drainage. Journal of Geophysical Research: Earth Surface, 123(6), Nature, 469(7331), 521–524. https://doi.org/10.1038/ 1430–1449. https://doi.org/10.1029/2017JF004304 nature09740 Pitcher, L. H., & Smith, L. C. (2019). Supraglacial streams Wolken, G. J., Sharp, M., & Wang, L. (2009). Snow and ice and rivers. Annual Review of Earth and Planetary facies variability and ice layer formation on Canadian Sciences, 47(1), 421–452. https://doi.org/10.1146/ Arctic ice caps, 1999–2005. Journal of Geophysical annurev-earth-053018-060212 Research: Earth Surface, 114(F3), F03011. https://doi. Poinar, K., Joughin, I., Das, S. B., Behn, M. D., Lenaerts, J., & org/10.1029/2008JF001173 Broeke, M. R. (2015). Limits to future expansion of sur- Wyatt, F. R. (2013). The spatial structure and temporal devel- face-melt-enhanced ice flow into the interior of western opment of supraglacial drainage systems, and their influ- Greenland. Geophysical Research Letters, 42(6), 1800– ence on the flow dynamics of high Arctic ice caps. 1807. https://doi.org/10.1002/2015GL063192 University of Alberta (Canada). RGI Consortium, R. (2017). Randolph Glacier Inventory–A Wyatt, F. R., & Sharp, M. J. (2015). Linking surface hydrol- Dataset of Global Glacier Outlines: Version 6.0: Technical ogy to flow regimes and patterns of velocity variability on Report, Global Land Ice Measurements from Space, Devon Ice Cap, Nunavut. Journal of Glaciology, 61(226), Colorado, USA. Digital Media. 387–399. https://doi.org/10.3189/2015JoG14J109 Richter-Menge, J., Osborne, E., & Jeffries, M. (2016). Arctic Yang, K., Li, M., Liu, Y., Cheng, L., Huang, Q., & Chen, Y. Report Card 2016. (2015b). River detection in remotely sensed imagery Röthlisberger, H., & Iken, A. (1981). Plucking as an effect of using Gabor filtering and path opening. Remote Sensing, water-Pressure variations at the glacier bed. Annals of 7(7), 8779–8802. https://doi.org/10.3390/rs70708779 Glaciology, 2,57–62. https://doi.org/10.3189/ Yang, K., & Smith, L. C. (2013). Supraglacial streams on the 172756481794352144 Greenland Ice Sheet delineated from combined spectral- Rutishauser, A., Grima, C., Sharp, M., Blankenship, D. D., shape information in high-resolution satellite imagery. Young, D. A., Cawkwell, F., & Dowdeswell, J. A. (2016). IEEE Geoscience and Remote Sensing Letters, 10(4), 801– Characterizing near-surface firn using the scattered signal 805. https://doi.org/10.1109/LGRS.2012.2224316 component of the glacier surface return from airborne Yang, K., & Smith, L. C. (2016). Internally drained catch- radio-echo sounding. Geophysical Research Letters, 43 ments dominate supraglacial hydrology of the southwest (24), 12502–512510. https://doi.org/10.1002/ Greenland Ice Sheet. Journal of Geophysical Research: 2016GL071230 Earth Surface, 121(10), 1891–1910. https://doi.org/10. Ryan, J. C., Smith, L. C., Van As, D., Cooley, S. W., Cooper, 1002/2016JF003927 M. G., Pitcher, L. H., & Hubbard, A. (2019). Greenland Yang, K., Smith, L. C., Chu, V. W., Gleason, C. J., & Li, M. Ice Sheet surface melt amplified by snowline migration (2015a). A caution on the use of surface digital elevation and bare ice exposure. Science Advances, 5 (3), models to simulate supraglacial hydrology of the green- eaav3738. https://doi.org/10.1126/sciadv.aav3738 land ice sheet. IEEE Journal of Selected Topics in Schoof, C. (2010). Ice-sheet acceleration driven by melt Applied Earth Observations and Remote Sensing, 8(11), supply variability. Nature, 468(7325), 803–806. https:// 5212–5224. https://doi.org/10.1109/JSTARS.2015. doi.org/10.1038/nature09618 2483483 Screen, J. A., & Simmonds, I. (2010). The central role of Yang, K., Smith, L. C., Chu, V. W., Pitcher, L. H., Gleason, diminishing sea ice in recent Arctic temperature amplifi- C.J.,Rennermalm,A.K.,&Li,M.(2016). Fluvial mor- cation. Nature, 464(7293), 1334–1337. https://doi.org/10. phometry of supraglacial river networks on the south- 1038/nature09051 west Greenland Ice Sheet. GIScience & Remote Sensing, Sharp, M., Burgess, D. O., Cogley, J. G., Ecclestone, M., 53(4), 459–482. https://doi.org/10.1080/15481603.2016. Labine, C., & Wolken, G. J. (2011). Extreme melt on 1162345 Canada’s Arctic ice caps in the 21st century. Yang, K., Smith, L. C., Fettweis, X., Gleason, C. J., Lu, Y., & Li, Geophysical Research Letters, 38(11), L11501. https:// M. (2019b). Surface meltwater runoff on the Greenland ice doi.org/10.1029/2011GL047381 sheet estimated from remotely sensed supraglacial lake Shepherd, A., Du, Z., Benham, T. J., Dowdeswell, J. A., & infilling rate. Remote Sensing of Environment, 234, Morris, E. M. (2007). Mass balance of Devon Ice Cap, 111459. https://doi.org/10.1016/j.rse.2019.111459 Canadian Arctic. Annals of Glaciology, 46, 249–254. Yang, K., Smith, L. C., Sole, A., Livingstone, S. J., Cheng, X., https://doi.org/10.3189/172756407782871279 Chen, Z., & Li, M. (2019a). Supraglacial rivers on the Smith, L. C., Chu, V. W., Yang, K., Gleason, C. J., Pitcher, L. northwest Greenland Ice Sheet, Devon Ice Cap, and H., Rennermalm, A. K., Legleiter, C. J., Behar, A. E., Barnes Ice Cap mapped using Sentinel-2 imagery. Overstreet, B. T., & Moustafa, S. E. (2015). Efficient melt- International Journal of Applied Earth Observation water drainage through supraglacial streams and rivers Geoinformation, 78,1–13. https://doi.org/10.1016/j.jag. on the southwest Greenland ice sheet. Proceedings of the 2019.01.008 National Academy of Sciences, 112(4), 1001–1006. Zuo, Z., & Oerlemans, J. (1996). Modelling albedo and https://doi.org/10.1073/pnas.1413024112 specific balance of the Greenland ice sheet: Calculations Smith, L. C., Yang, K., Pitcher, L. H., Overstreet, B. T., Chu, for the søndre Strømfjord transect. Journal of V. W., Rennermalm, ÅK, Ryan, J. C., Cooper, M. G., Glaciology, 42(141), 305–317. https://doi.org/10.1017/ Gleason, C. J., & Tedesco, M. (2017). Direct S0022143000004160